非参数互信息和多维尺度分析在滚动轴承故障诊断中的应用

Application of Non-parametric Mutual Information and Multidimensional Scaling in Rolling Bearing Fault Diagnosis

针对轴承不同故障状态难以识别的问题,将特征选择方法应用于滚动轴承故障诊断。在互信息方法的基础上提出非参数互信息(NPMI)的特征选择方法:首先从原始信号中提取能够表征轴承运行状态变化的时频域统计特征并建立多域特征集;然后利用NPMI特征选择方法去除特征集中的无关特征和冗余特征,建立敏感特征集,再利用多维尺度分析对敏感特征集进行降维可视化处理,比较特征的类别可分及聚类能力;最后将降维后的特征向量输入到支持向量机中得到不同故障的识别结果。以分类器正确率为依据,验证了基于非参数互信息特征选择方法的有效性和优越性。

In view of the difficulty of identifying different fault states of bearings,the feature selection method was applied to rolling bearing fault diagnosis.A feature selection method based on non-parametric mutual information(NPMI)was presented on the basis of mutual information method.First,the statistical characteristics in time and frequency domains that could represent the change of the bear-ing state were extracted from the original signal and the multi-domain feature set was set up.Then,the NPMI feature selection method was used to remove the unrelated features and redundant features in the feature set,the sensitive feature set was established,and multidi-mensional scaling was used to visualize the sensitive feature sets,then the classification and clustering ability of the features were com-pared.Finally,the feature vectors after dimension reduction were input into the support vector machine to get the recognition results of different faults.Based on the accuracy of classifier,the validity and superiority of feature selection method based on non-parametric mutu-al information was verified.